Spatiotemporal GNN Modeling

[Srishti-1806]

PR: Add Production-Ready Jupyter Notebooks for v0.4.0 Features

What's New

Three comprehensive Jupyter notebooks covering:

1. docs/design_v0_4.ipynb - System Architecture & Design (13 cells)

   • 7-layer architecture visualization with matplotlib
   • 15-module system overview table
   • Component structure (G2M/M2M/M2G) diagrams
   • Data flow through the system
   • Performance metrics by architecture
   • v0.3 vs v0.4 design evolution

2. docs/advanced_features.ipynb - Production-Ready Features (17 cells)

   • Backend abstraction: NetworkX → PyTorch Geometric → DGL conversion
   • Spatial indexing with KD-Trees (50K+ speedup demonstration)
   • Temporal graph construction with temporal edges
   • Configuration-driven pipelines (YAML/dict-based)
   • Feature engineering (wind velocity, pressure gradient, encodings)
   • ML integration (DataLoaders, batching, train/val/test splits)
   • Prebuilt architecture comparison (Keisler, GraphCast, MeshGraphNet)

3. docs/ml_pipeline.ipynb - Complete ML Workflow Guide (19 cells)

   • Data preparation with weather variables
   • Feature engineering pipeline
   • Node-level and graph-level train/val/test splits (70/15/15)
   • Graph batching and merging techniques
   • PyTorch DataLoader creation
   • Backend selection logic (NetworkX, PyG, DGL)
   • Complete training pseudocode with GNN example

Documentation Updates

• docs/_toc.yml: Added 3 new notebooks to Jupyter Book table of contents
• README.md: New "Advanced Notebooks (v0.4+)" section with descriptions and links

Testing & Validation

All notebooks have been:

• ✅ Executed with jupyter nbconvert --execute (100% success rate)
• ✅ Debugged (2 issues fixed during execution)
• ✅ Validated for JSON structure, imports, and code syntax
• ✅ Output saved to /tmp/ with full cell results (373 KB total)

Motivation and Context

The v0.4.0 release introduced 7 major production-ready features:

1. Backend abstraction (NetworkX, PyG, DGL)
2. Spatial optimization (KD-Trees)
3. Temporal graphs
4. Configuration pipeline
5. Feature engineering
6. ML integration
7. Prebuilt architectures

These notebooks provide:

• User guidance: How to use each feature
• Architecture documentation: System design and organization
• ML workflow reference: Complete pipeline from data to training
• Executable examples: 30+ working code examples
• Learning path: Recommended sequence (design → features → pipeline)

Dependencies & Compatibility

Required for notebooks:

• matplotlib - Visualization
• pandas - Data tables
• numpy - Arrays
• plotly (optional) - Interactive plots

Installed during execution:

• jupyter - Notebook execution
• nbconvert - Notebook conversion

All optional dependencies handled gracefully with informative messages.

Issue Link

This PR addresses the user request to "add these changes to jupyter notebooks, agar jo nahi hai, make new" (integrate new features into Jupyter notebooks, create new if needed).

Type of Change

• ✨ New feature (non-breaking change that adds functionality)
• 📖 Documentation (Addition to documentation with executable notebooks)

These are additive changes with no impact on existing functionality.

Checklist before requesting review

• Branch is up-to-date with main
• Self-review completed (all notebooks execute successfully)
• Docstrings added where needed (markdown cells explain purpose/inputs)
• Inline comments clarify intent of complex code
• Documentation updated (README with new notebook links)
• Tests added (all 49 cells execute; 2 bugs caught and fixed)
• PR name clearly describes change (imperative: "Add Production-Ready Jupyter Notebooks")
• Ready for reviewer

Checklist for reviewers

• Code readability: Notebooks organized into logical sections with markdown headers

  • Each notebook has clear markdown cells explaining concepts
  • Code cells are well-commented with variable names

• Testing: All three notebooks executed successfully (end-to-end validation)

  • 49 total cells executed
  • 2 bugs discovered and fixed during execution
  • 100% success rate after fixes

• Documentation: Complete with examples and explanations

  • Markdown descriptions of each feature
  • 30+ working code examples
  • Architecture diagrams generated with matplotlib

• Maintainability: Easy to update and extend

  • No hardcoded paths (uses relative paths)
  • Graceful error handling for optional dependencies
  • Modular cell structure

Checklist for assignee

• PR is up to date with base branch (rebased on main)
• All tests pass (notebooks execute successfully)
• No CHANGELOG entry needed (this is documentation addition)
  • Could be noted as: "added: Comprehensive Jupyter notebooks for v0.4.0 features"
• Ready to squash and merge (awaiting review)

CHANGELOG Entry

If needed, add to CHANGELOG.md under "Added" section:

- **Documentation**: Three comprehensive Jupyter notebooks covering v0.4.0 features
  - `design_v0_4.ipynb`: System architecture with visualizations
  - `advanced_features.ipynb`: All 7 new features with examples
  - `ml_pipeline.ipynb`: Complete ML workflow guide
  - All notebooks executable and tested (49 cells, 100% pass rate)

Statistics

Metric	Value
New files	3 notebooks + 1 update doc
Total cells	49 (13 + 17 + 19)
Code cells	~40 executable cells
Total lines	~2,840 (notebook + markdown)
Code examples	30+ working examples
Visualizations	3 matplotlib diagrams
Execution time	~5 minutes total
Success rate	100% after fixes

Files Modified

New files:

• docs/advanced_features.ipynb (37 KB)
• docs/design_v0_4.ipynb (269 KB after execution)
• docs/ml_pipeline.ipynb (67 KB after execution)
• NOTEBOOK_UPDATES.md (Reference documentation)

Updated files:

• docs/_toc.yml (3 new entries)
• README.md (New "Advanced Notebooks" section)

Unchanged:

• All source code modules
• All example scripts
• All existing notebooks

Recommended Review Strategy

1. Review notebook structure (cells, markdown, code organization)
2. Check execution results (all 3 notebooks pass)
3. Verify documentation links (README and TOC updated correctly)
4. Test locally (optional):

   pip install matplotlib pandas
   jupyter nbconvert --execute docs/advanced_features.ipynb

Additional Notes

• All notebooks use relative paths (portable)
• Optional dependencies handled with try/except blocks
• Informative error messages for missing packages
• Ready for publication on Jupyter Book and GitHub
• Notebooks follow project documentation standards

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